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The Problem-Substrate Mapping: A Framework for Honest Computational Investment

Rowan Brad Quni-Gudzinas

Published Jul 8, 2026• 0 citations
Publisher

Abstract

Phase IV of the Qubit Delusion series. Translates the critique into a concrete investment portfolio: matching computational problem classes (optimization, linear algebra, probabilistic inference, quantum simulation, cryptography, general-purpose) to optimal physical substrates (Ising machines, optical processors, neuromorphic, analog quantum simulators, reversible classical, fault-tolerant QC). Proposes a standardized five-question evaluation framework with red flags and green flags. The resulting portfolio allocates ~45% to thermodynamic/analog, 25% to optical, 20% to neuromorphic, and ~10% to quantum — a near-inversion of current R&D allocation where gate-model QC absorbs ~60%. Full text and updates: https://papers.qnfo.org/papers/paper-problem-substrate-mapping/

DOI
10.5281/zenodo.21255345

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Phase IV of the Qubit Delusion series. Translates the critique into a concrete investment portfolio: matching computational problem classes (optimization, linear algebra, probabilistic inference, quantum simulation, cryptography, general-purpose) to optimal physical substrates (Ising machines, optical processors, neuromorphic, analog quantum simulators, reversible classical, fault-tolerant QC). Proposes a standardized five-question evaluation framework with red flags and green flags. The resulting portfolio allocates ~45% to thermodynamic/analog, 25% to optical, 20% to neuromorphic, and ~10% to quantum — a near-inversion of current R&D allocation where gate-model QC absorbs ~60%. Full text and updates: https://papers.qnfo.org/papers/paper-problem-substrate-mapping/
--- title: "Manifesto for Honest Computation" author: "QNFO Research Collective" date: 2026-07-08 series: "The Qubit Delusion --- Phase VI (Final Synthesis)" abstract: > Five papers have traced an arc from diagnosis to prescription. The qubit is a scaffold, not an invariant. Alternative paradigms exist. Physical limits are real. A diversified portfolio is rational. Institutions must be reformed. This final paper --- the Manifesto --- synthesizes these findings into principles for honest computation and a call to action for investors, researchers, policymakers, and the public. We propose five principles: (1) the substrate IS the algorithm; (2) correlation over particle; (3) joules per solution as the universal metric; (4) falsifiability as a condition of funding; and (5) institutional independence of verification. We propose a specific resource reallocation --- from the ~60% currently consumed by gate-model quantum computing toward thermodynamic, optical, neuromorphic, and analog quantum simulation --- and a Falsification Pledge by which institutions commit to pre-registering computational claims with time-bound success criteria. Computation is too important to be left to narrative production. The future of computing is honest, or it is nothing. keywords: - manifesto - honest computation - post-classical computing - quantum computing reform - computational principles - research ethics ---
Companion paper to "The Qubit Delusion" (v1.1, 10.5281/zenodo.21254143). If the qubit-gate-circuit model is an epistemic failure — a projection of particle ontology onto relational, field-theoretic reality — what comes next? We strip quantum mechanics to its minimal ontological commitments, survey alternative paradigms (MBQC, CV-QC, TQC, field-theoretic, analog) for ontological fidelity, assess commercial manufacturability of each physical platform, and ask whether "quantum computing" is even the right frame. The deeper lesson — that computation IS a physical process and the substrate IS the algorithm — points toward thermodynamic, neuromorphic, and optical architectures where physics does the computing directly.
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Portfolio Optimization (PO) is a financial problem aiming to maximize the net gains while minimizing the risks in a given investment portfolio. The novelty of Quantum algorithms lies in their acclaimed potential and capability to solve complex problems given the underlying Quantum Computing (QC) infrastructure. Utilizing QC's applicable strengths to the finance industry's problems, such as PO, allows us to solve these problems using quantum-based algorithms such as Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization Algorithm (QAOA). While the Quantum potential for finance is highly impactful, the architecture and composition of the quantum circuits have not yet been properly defined as robust financial frameworks/algorithms as state of the art in present literature for research and design development purposes. In this work, we propose a novel scalable framework, denoted PO-QA, to systematically investigate the variation of quantum parameters (such as rotation blocks, repetitions, and entanglement types) to observe their subtle effect on the overall performance. In our paper, the performance is measured and dictated by convergence to similar ground-state energy values for resultant optimal solutions by each algorithm variation set for QAOA and VQE to the exact eigensolver (classical solution). Our results provide effective insights into comprehending PO from the lens of Quantum Machine Learning in terms of convergence to the classical solution, which is used as a benchmark. This study paves the way for identifying efficient configurations of quantum circuits for solving PO and unveiling their inherent inter-relationships.
This survey presents a system-level architectural analysis of quantum computing for portfolio optimization, an NP-hard problem in FinTech facing scalability challenges in classical cloud infrastructures. Based on a systematic review of 30 representative papers (2020-2025), three dominant paradigms—Quantum Annealing (QA), Variational Quantum Algorithms (VQAs), and Quantum Machine Learning (QML)—are evaluated specifically by focusing on their constraint enforcement strategies and latency profiles . The analysis suggests that QA currently demonstrates significant near-term operational readiness for constraint-heavy portfolios due to its native solvers and lower communication overhead compared to iterative gate-based methods. Conversely, while VQAs offer high expressivity via mixers, they are operationally limited by the communication complexity of hybrid loops over public networks. Furthermore, QML approaches are shown to shift the bottleneck from optimization to state preparation, where data encoding latency often negates inference advantages. These engineering insights are synthesized into a practical implementation roadmap, advocating for distributed hybrid architectures to bridge the gap between theoretical quantum advantage and low-latency financial deployment.
A significant challenge in quantum computing (QC) is developing learning models that truly align with quantum principles, as many current approaches are complex adaptations of classical frameworks. In this work, we introduce Quantum Hyperdimensional Computing (QHDC), a fundamentally new paradigm. We demonstrate that the core operations of its classical counterpart, Hyperdimensional Computing (HDC), a brain-inspired model, map with remarkable elegance and direct correspondence onto the native operations of a QC. This suggests HDC is exceptionally well-suited for a quantum-native implementation. We establish a direct, resource-efficient mapping: (i) hypervectors are mapped to quantum states, (ii) the bundling operation is implemented as a quantum-native averaging process using a Linear Combination of Unitaries (LCU) and Oblivious Amplitude Amplification (OAA), (iii) the binding operation is realized via quantum phase oracles, (iv) the permutation operation is implemented using the Quantum Fourier Transform (QFT), and (v) vector similarity is calculated using quantum state fidelity measurements based on the Hadamard Test. We present the first-ever implementation of this framework, validated through symbolic analogical reasoning and supervised classification tasks. The viability of QHDC is rigorously assessed via a comparative analysis of results from classical computation, ideal quantum simulation, and execution of a 156-qubit IBM Heron r3 quantum processor. Our results validate the proposed mappings and demonstrate the versatility of the framework, establishing QHDC as a physically realizable technology. This work lays the foundation for a new class of quantum neuromorphic algorithms and opens a promising avenue for tackling complex cognitive and biomedical problems intractable for classical systems.